I have the following `pandas`

data frame:

```
import numpy as np
import pandas as pd
timestamps = [1, 14, 30]
data = dict(quantities=[1, 4, 9], e_quantities=[1, 2, 3])
df = pd.DataFrame(data=data, columns=data.keys(), index=timestamps)
```

which looks like this:

```
quantities e_quantities
1 1 1
14 4 2
30 9 3
```

However, the `timestamps`

should run from 1 to 52:

```
index = pd.RangeIndex(1, 53)
```

The following line provides the `timestamps`

that are missing:

```
series_fill = pd.Series(np.nan, index=index.difference(df.index)).sort_index()
```

**How can I get the quantities and e_quantities columns to have NaN values at these missing timestamps?**

I've tried:

```
df = pd.concat([df, series_fill]).sort_index()
```

but it adds another column (`0`

) and swaps the order of the original data frame:

```
0 e_quantities quantities
1 NaN 1.0 1.0
2 NaN NaN NaN
3 NaN NaN NaN
```

Thanks for any help here.

I think you are looking for

`reindex`